Hot-deformation kinetics analysis and extrusion parameter optimization of a dilute rare-earth free magnesium alloy

The fundamental research on thermo-mechanical conditions provides an experimental basis for high-performance Mg-Al-Ca-Mn alloys. However, there is a lack of systematical investigation for this series alloys on the hot-deformation kinetics and extrusion parameter optimiza-tion. Here, the flow behavior, constitutive model, dynamic recrystallization (DRX) kinetic model and processing map of a dilute rare-earth free Mg-1.3Al-0.4Ca-0.4Mn (AXM100, wt.%) alloy were studied under different hot-compressive conditions. In addition, the extrusion pa-rameter optimization of this alloy was performed based on the hot-processing map. The results showed that the conventional Arrhenius-type strain-related constitutive model only worked well for the flow curves at high temperatures and low strain rates. In comparison, using the machine learning assisted model (support vector regression, SVR) could effectively improve the accuracy between the predicted and experi-mental values. The DRX kinetic model was established, and a typical necklace-shaped structure preferentially occurred at the original grain boundaries and the second phases. The DRX nucleation weakened the texture intensity, and the further growth caused the more scattered basal texture. The hot-processing maps at different strains were also measured and the optimal hot-processing range could be confirmed at the deformation temperatures of 600 similar to 723 K and the strain rates of 0.018 similar to 0.563 s -1. Based on the optimum hot-processing range, a suitable extrusion parameter was considered as 603 K and 0.1 mm/s and the as-extruded alloy in this parameter exhibited a good strength-ductility synergy (yield strength of similar to 232.1 MPa, ultimate strength of similar to 278.2 MPa and elongation-to-failure of similar to 20.1%). Finally, the strengthening-plasticizing mechanisms and the relationships between the DRXed grain size, yield strength and extrusion parameters were analyzed.(c) 2022 Chongqing University. Publishing services provided by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Peer review under responsibility of Chongqing University